Module `pacai.core.mdp`

Expand source code

import abc

class MarkovDecisionProcess(abc.ABC):
    @abc.abstractmethod
    def getStates(self):
        """
        Return a list of all states in the MDP.
        Not generally possible for large MDPs.
        """

        pass

    @abc.abstractmethod
    def getStartState(self):
        """
        Return the start state of the MDP.
        """

        pass

    @abc.abstractmethod
    def getPossibleActions(self, state):
        """
        Return list of possible actions from 'state'.
        """

        pass

    @abc.abstractmethod
    def getTransitionStatesAndProbs(self, state, action):
        """
        Returns list of (nextState, prob) pairs representing the states reachable
        from 'state' by taking 'action' along with their transition probabilities.

        Note that in Q-Learning and reinforcment learning in general,
        we do not know these probabilities nor do we directly model them.
        """

        pass

    @abc.abstractmethod
    def getReward(self, state, action, nextState):
        """
        Get the reward for the state, action, nextState transition.

        Not available in reinforcement learning.
        """

        pass

    @abc.abstractmethod
    def isTerminal(self, state):
        """
        Returns true if the current state is a terminal state.
        By convention, a terminal state has zero future rewards.
        Sometimes the terminal state(s) may have no possible actions.
        It is also common to think of the terminal state as having
        a self-loop action 'pass' with zero reward; the formulations are equivalent.
        """

        pass

Classes

class MarkovDecisionProcess (*args, **kwargs)

Helper class that provides a standard way to create an ABC using inheritance.

Expand source code

class MarkovDecisionProcess(abc.ABC):
    @abc.abstractmethod
    def getStates(self):
        """
        Return a list of all states in the MDP.
        Not generally possible for large MDPs.
        """

        pass

    @abc.abstractmethod
    def getStartState(self):
        """
        Return the start state of the MDP.
        """

        pass

    @abc.abstractmethod
    def getPossibleActions(self, state):
        """
        Return list of possible actions from 'state'.
        """

        pass

    @abc.abstractmethod
    def getTransitionStatesAndProbs(self, state, action):
        """
        Returns list of (nextState, prob) pairs representing the states reachable
        from 'state' by taking 'action' along with their transition probabilities.

        Note that in Q-Learning and reinforcment learning in general,
        we do not know these probabilities nor do we directly model them.
        """

        pass

    @abc.abstractmethod
    def getReward(self, state, action, nextState):
        """
        Get the reward for the state, action, nextState transition.

        Not available in reinforcement learning.
        """

        pass

    @abc.abstractmethod
    def isTerminal(self, state):
        """
        Returns true if the current state is a terminal state.
        By convention, a terminal state has zero future rewards.
        Sometimes the terminal state(s) may have no possible actions.
        It is also common to think of the terminal state as having
        a self-loop action 'pass' with zero reward; the formulations are equivalent.
        """

        pass

Ancestors

abc.ABC

Subclasses

Gridworld

Methods

def getPossibleActions(self, state)

Return list of possible actions from 'state'.

Expand source code

@abc.abstractmethod
def getPossibleActions(self, state):
    """
    Return list of possible actions from 'state'.
    """

    pass

def getReward(self, state, action, nextState)

Get the reward for the state, action, nextState transition.

Not available in reinforcement learning.

Expand source code

@abc.abstractmethod
def getReward(self, state, action, nextState):
    """
    Get the reward for the state, action, nextState transition.

    Not available in reinforcement learning.
    """

    pass

def getStartState(self)

Return the start state of the MDP.

Expand source code

@abc.abstractmethod
def getStartState(self):
    """
    Return the start state of the MDP.
    """

    pass

def getStates(self)

Return a list of all states in the MDP. Not generally possible for large MDPs.

Expand source code

@abc.abstractmethod
def getStates(self):
    """
    Return a list of all states in the MDP.
    Not generally possible for large MDPs.
    """

    pass

def getTransitionStatesAndProbs(self, state, action)

Returns list of (nextState, prob) pairs representing the states reachable from 'state' by taking 'action' along with their transition probabilities.

Note that in Q-Learning and reinforcment learning in general, we do not know these probabilities nor do we directly model them.

Expand source code

@abc.abstractmethod
def getTransitionStatesAndProbs(self, state, action):
    """
    Returns list of (nextState, prob) pairs representing the states reachable
    from 'state' by taking 'action' along with their transition probabilities.

    Note that in Q-Learning and reinforcment learning in general,
    we do not know these probabilities nor do we directly model them.
    """

    pass

def isTerminal(self, state)

Returns true if the current state is a terminal state. By convention, a terminal state has zero future rewards. Sometimes the terminal state(s) may have no possible actions. It is also common to think of the terminal state as having a self-loop action 'pass' with zero reward; the formulations are equivalent.

Expand source code

@abc.abstractmethod
def isTerminal(self, state):
    """
    Returns true if the current state is a terminal state.
    By convention, a terminal state has zero future rewards.
    Sometimes the terminal state(s) may have no possible actions.
    It is also common to think of the terminal state as having
    a self-loop action 'pass' with zero reward; the formulations are equivalent.
    """

    pass